In one of the main fields of application of the present method, namely the field of digital subtraction angiography, blood vessels in the human body are captured and displayed using the imaging system, in this instance an X-ray unit. With this method series of X-ray images of the relevant examination area of the patient are recorded, while a contrast agent is injected to highlight the vessels (fill images). An image of the examination area is also recorded without injection of a contrast agent (mask image). By digitally subtracting the mask image from the respective fill images subtraction images are obtained, on which only the vessels can be identified, while subtraction causes other X-ray-absorbent structures, e.g. bones, superimposed thereon to disappear.
Image subtraction primarily assumes that the images in question were recorded under the same geometric conditions, so that they are congruent. Motion of the mapped structures between the individual recordings can lead to interference from motion artifacts in the subtracted images. These can be caused by motion of the patient between the recording of the mask image and the recording of the fill images. One consequence of such motion can be that the resulting subtraction image can no longer be used for diagnosis. In practice this can mean therefore that subtraction images subject to interference from such motion artifacts have to be repeated. This is associated with additional outlay of time and contrast agents and additional exposure of the patient to radiation.
One method used with digital subtraction angiography is the so-called pathfinder technique, also referred to as road mapping. This technique is used for the selective catheterization of vessels during interventional therapy. During such vascular interventions the current position of an X-ray-absorbent catheter is displayed by means of fluoroscopy in a two-dimensional image. In order also to be able to identify the blood vessel as a so-called road map, at the start of the intervention an image is recorded, in which a small quantity of contrast agent has been injected. This image is used as the mask image. The subsequent fluoroscopy images obtained without injecting a contrast agent are each subtracted from the mask image. This produces subtraction images, in which the catheter can be identified as a light from against the dark blood vessel and the background has been eliminated by subtraction.
Road mapping is also subject to interference due to motion of the mapped structures during series recordings in a similar manner to digital subtraction angiography. Motion between the recording of the mask image and the respective fluoroscopy image gives rise to two problems here. On the one hand the background is no longer correctly subtracted, so image artifacts result. On the other hand it can happen that the position of the catheter in relation to the displayed blood vessel, as determined by means of the image, is not correct. This serious error can for example result in the catheter being shown outside the vessel in the image, even though it is actually inside the vessel. In extreme cases such false representations can lead to errors in catheter control and cause damage to the vessel. If the patient moves during the intervention, the road map therefore frequently has to be refreshed by a repeat recording of a mask image. This requires additional time outlay and contrast agent consumption and is associated with a higher radiation dose for the patient.
Various solutions are currently known to avoid or alleviate this problem. Essentially there are 3 different approaches.
Patient-related solutions have the aim of preventing patient motion during recording. Thus in the case of thoracic examinations for example the patient is trained to hold their breath during series recordings. A further option is that of preventing some sources of motion artifacts by general anesthesia. One disadvantage of the patient-related method is that it is either only partially effective or it cannot always be used. General anesthesia is for example associated with a number of risks and is there fore not medically indicated for many applications of digital subtraction angiography. On the other hand even with general anesthesia some sources of motion artifacts remain, e.g. respiratory motion.
In the case of solutions relating to imaging recording, image recording is implemented such that motion artifacts are minimized. To date so-called gating methods have primarily been known for this purpose, with which recording is linked to physiological measurement. For example in the case of ECG gating, images are only acquired in a specific cardiac phase, so that heart motion is compensated for. Gating methods can however only be used for a few specific applications and can only prevent motion artifacts caused by specific sources, for which physiological signals can be measured.
A further approach to reducing motion artifacts involves retrospective image processing of the recorded images. These techniques aim to obtain a better correspondence of the mask image and the fill image by appropriate image processing. The simplest technique used is so-called pixel shifting or subpixel shifting, in which the user shifts the mask image toward the fill image manually in two dimensions, until minimization of the motion artifacts in the subtraction image is achieved. This method is implemented in all commercial angiography systems. Also automatic methods, which establish the best correspondence based on quantifiable similarity measures, are present in some commercial angiography systems. More complex methods do not use overall pixel shifting over the entire image area but optimize local areas of the image separately from each other, as disclosed for example in U.S. Pat. No. 4,870,692 A. Also numerous more complex methods for motion correction are proposed in the scientific literature. These are essentially optimization methods, in which the aim is to find the transformation between mask image and fill image, which produces the fewest motion artifacts. Further examples of retrospective image processing can be found in the publications “Motion-compensated digital subtraction angiography”, Magnus Hemmendorff et al., SPIE '99, San Diego USA, Proceedings of SPIE's International Symposium on Medical Imaging 1999, Volume 3661, Image Processing, February 1999, pp. 1396-1405; Meijering E. H. et al., “Reduction of patient motion artefacts in digital subtraction angiography: evaluation of a fast and fully automatic technique”, Radiology, 2001 April, 219(1): 288-293; or “Retrospective Motion Correction in Digital Subtraction Angiography: A Review”, Erik H. W. Meijering et al., IEEE Transactions on Medical Imaging, Vol. 18, No. 1, January 1999, pp. 2-21.
Retrospective image processing can however only compensate approximately for motion. Arbitrary motion cannot be corrected. Even with a restriction to a correction of 6 degrees of freedom corresponding to the rotation and translation of a rigid element, it is not possible to determine motion uniquely from the two-dimensional images. The complex image processing methods also require a great deal of computation time and cannot therefore easily be implemented in real time. Manual image processing methods (pixel shifting) require user interaction and can take a lot of time. They can also essentially only be used for subsequent improvement of DSA images, as with road mapping there is little time for interaction.
DE 100 51 370 A1 covers a method for the precise positioning of a patient in radiotherapy or radiosurgery. In this field a computer tomograph is used to generate a three-dimensional image data set of the examination area, on the basis of which subsequent radiotherapy, for example the irradiation of a tumor, is planned. The patient then has to be positioned as precisely as possible in relation to the linear accelerator required for radiotherapy, so that irradiation takes place as exactly as possible at the planned position. With this publication the most precise positioning possible is achieved by recording X-ray fluoroscopy images from two different directions at the linear accelerator, which are then use d to determine the correspondence of position or difference in position by comparison with correspondingly reconstructed (virtual) fluoroscopy images from the previously generated 3D image data set. The position of the patient can then be adjusted to compensate for this position difference by moving the patient table. The patient is pre-positioned by means of a computer and camera controlled navigation and tracking system with the assistance of synthetic markers on the patient.
Like DE 100 51 370 A1, DE 102 50 655 A1 discloses a patient positioning system for the same purpose. To solve the positioning problem, in this publication a surface image generator is used both at the CT device and at the linear accelerator, the images of which are compared and used to position the patient precisely.